The authors in [1]} regard the output of the last convolutional layer with \(H \times W \times D\)  map as a set of \(D\) -dimensional descriptors at \(H \times W\)  spatial locations in input image which are further embedded and pooled by the NetVLAD layer.
However, by using ResNet-20 with image patches of size \(32 \times 32\)  as feature extractor, output of the last convolutional layer becomes \(1 \times 1 \times 64\)  map which we consider as 64-dimensional local image descriptor extracted from the input image patch. Passing this descriptor (with \(H =1\)  and \(W = 1\) ) enables NetVLAD layer to learn the respective local image descriptor embedding. So in this manner, the NetVLAD layer functions as generalized VLAD embedding. \(\ell _{2}\) -normalization is employed prior to learning from semi-hard triplets.
